Method and system for controlling settings of an ultrasound scanner

ABSTRACT

During acquisition of an ultrasound image feed, ultrasound control data frames are acquired that may be interspersed amongst the ultrasound data frames. The control data frames may use consistent reference scan parameters, irrespective of the scanner settings, and may not need to be converted to image frames. The control data frames can be passed to an artificial intelligence model, which predicts the suitable settings for scanning the anatomy that is being scanned. The artificial intelligence model can be trained with a dataset containing different classes of ultrasound control data frames for different settings, where substantially all the ultrasound control data frames in the dataset are consistently acquired using the reference scan parameters.

TECHNICAL FIELD

This disclosure relates to viewing ultrasound images. In particular, itrelates to systems and methods for controlling settings of an ultrasoundscanner.

BACKGROUND

Ultrasound is a useful, non-invasive imaging technique capable ofproducing real time images of internal structures within tissue.Ultrasound imaging has an advantage over X-ray imaging in thatultrasound imaging does not involve ionizing radiation. Some mobileultrasound scanners, including app-based ultrasound scanners, require anadd-on device that can act as both as a display and a control device.Examples of these add-on devices are mobile phones, tablets, laptops ordesktop computers.

When using some ultrasound scanners, whether mobile or not, users aretraditionally expected to select a preset depending on the part of theanatomy that is to be scanned. The preset is associated with a set ofparameters that instruct the ultrasound scanner how to acquire andprocess the ultrasound data. The set of parameters for each preset isusually optimized for the particular body part to which the presetrelates. There may be upwards of a hundred different parameters(including, for example, frequency, focal zones, line density, whetherharmonic imaging is on, and the like) for each preset depending on theultrasound scanner.

In some cases, for example in an emergency room, in a field hospital, orif a user is unfamiliar with the particular ultrasound scanner, thepreset may be incorrectly selected. This may happen for various reasons.For example, this may unintentionally done by the operator, or it may beleft on a prior setting, or it may be not set and the scanner left in adefault mode. Additionally or alternatively, if different areas of thebody need to be scanned in one session, the user may forget to switchthe preset when moving to a different body area. The result may be, forexample, that the ultrasound image that is generated is not optimal,and/or the ultrasound scanner uses more power than necessary.

There is therefore a need to ensure that a preset of an ultrasoundscanner is correctly selected for the part of the anatomy that is beingscanned.

The above background information is provided to reveal informationbelieved by the applicant to be of possible relevance to the presentinvention. No admission is necessarily intended, nor should beconstrued, that any of the preceding information constitutes prior artagainst the present invention. The embodiments discussed herein mayaddress and/or ameliorate one or more of the aforementioned drawbacksidentified above. The foregoing examples of the related art andlimitations related thereto are intended to be illustrative and notexclusive. Other limitations of the related art will become apparent tothose of skill in the art upon a reading of the specification and astudy of the drawings herein.

BRIEF DESCRIPTION OF DRAWINGS

The following drawings illustrate embodiments of the invention andshould not be construed as restricting the scope of the invention in anyway.

FIG. 1 is a schematic diagram of a system according to an embodiment ofthe present invention.

FIG. 2 is a schematic diagram showing a series of control and imageframes and their analysis, according to an embodiment of the presentinvention.

FIG. 3 is a flowchart for controlling an ultrasound scanner according toan embodiment of the present invention.

FIG. 4 is a flowchart for training an AI model, according to anembodiment of the present invention.

FIG. 5 is a flowchart for reinforcing an AI model, according to anembodiment of the present invention.

FIG. 6 is a flowchart for identifying features in an ultrasound image,according to an embodiment of the present invention.

FIG. 7 is a schematic diagram of a further system according to anembodiment of the present invention.

DETAILED DESCRIPTION A. Glossary

The term “AI model” means a mathematical or statistical model that maybe generated through artificial intelligence techniques such as machinelearning. For example, the machine learning may involve inputtinglabeled or classified data into a neural network algorithm for training,so as to generate a model that can make predictions or decisions on newdata without being explicitly programmed to do so. Different softwaretools (e.g., TensorFlow™, PyTorch™, Keras™) may be used to performmachine learning processes.

The term “depth” when relating to an ultrasound image refers to ameasure of how far into the structure being scanned (e.g., tissue or aphantom) a given ultrasound image shows.

The term “module” can refer to any component in this invention and toany or all of the features of the invention without limitation. A modulemay be a software, firmware or hardware module, and may be located, forexample, in the ultrasound scanner, a display device or a server.

The term “network” can include both a mobile network and data networkwithout limiting the term's meaning, and includes the use of wireless(e.g. 2G, 3G, 4G, 5G, WiFi™, WiMAX™, Wireless USB (Universal SerialBus), Zigbee™, Bluetooth™ and satellite), and/or hard wired connectionssuch as local, internet, ADSL (Asymmetrical Digital Subscriber Line),DSL (Digital Subscriber Line), cable modem, T1, T3, fiber-optic, dial-upmodem, television cable, and may include connections to flash memorydata cards and/or USB memory sticks where appropriate. A network couldalso mean dedicated connections between computing devices and electroniccomponents, such as buses for intra-chip communications.

The term “operator” (or “user”) may refer to the person that isoperating an ultrasound scanner (e.g., a clinician, medical personnel, asonographer, ultrasound student, ultrasonographer and/or ultrasoundtechnician).

The term “processor” can refer to any electronic circuit or group ofcircuits that perform calculations, and may include, for example, singleor multicore processors, multiple processors, an ASIC (ApplicationSpecific Integrated Circuit), and dedicated circuits implemented, forexample, on a reconfigurable device such as an FPGA (Field ProgrammableGate Array). A processor may perform the steps in the flowcharts andsequence diagrams, whether they are explicitly described as beingexecuted by the processor or whether the execution thereby is implicitdue to the steps being described as performed by the system, a device,code or a module. The processor, if comprised of multiple processors,may be located together or geographically separate from each other. Theterm includes virtual processors and machine instances as in cloudcomputing or local virtualization, which are ultimately grounded inphysical processors.

The term “scan convert”, “scan conversion”, or any of its grammaticalforms refers to the construction of an ultrasound media, such as a stillimage or a video, from lines of ultrasound scan data representing echoesof ultrasound signals. Scan conversion may involve converting beamsand/or vectors of acoustic scan data which are in polar (R-theta)coordinates to cartesian (X-Y) coordinates.

The term “system” when used herein, and not otherwise qualified, refersto a system for controlling the settings of an ultrasound scanner usingdata obtained in control data frames acquired between image data frames,the system being a subject of the present invention. The system mayinclude a scanner and a display device, or a scanner, display device anda server.

The term “ultrasound control data frame” (or “control data frame” forbrevity) refers to a frame of ultrasound data that is captured by anultrasound scanner. The ultrasound control data frame has the form ofmultiple lines of data that each represent echoes of ultrasound.Ultrasound control data frames may all be acquired with consistent,reference scan parameters, unlike ultrasound data frames, which may beacquired with different parameters depending on the settings of theultrasound scanner. Ultrasound control data frames are not usuallyconverted to viewable image frames.

The term “ultrasound data frame” (or “image data frame”) refers to aframe of ultrasound data that is captured by an ultrasound scanner. Theultrasound data frame typically has the form of multiple lines of datathat each represent echoes of ultrasound. Ultrasound data frames areusually acquired with different sets of scan parameters, where each setdepends on which preset of the ultrasound scanner is selected.Ultrasound data frames are usually converted to viewable image framesfor viewing by an operator of the ultrasound scanner.

The term “ultrasound image frame” (or “image frame”) refers to a frameof post-scan conversion data that is suitable for rendering anultrasound image on a screen or other display device.

B. Exemplary Embodiments

Referring to FIG. 1, an exemplary system 10 is shown for controlling thesettings of an ultrasound scanner 12 (hereinafter “scanner” for brevity)dependent on interspersed control data frames. The system 10 includes anultrasound scanner 12 with a processor 14, which is connected to anon-transitory computer readable memory 16 storing computer readableinstructions 18, which, when executed by the processor 14, may cause thescanner 12 to provide one or more of the functions of the system 10.Such functions may be, for example, the acquisition of ultrasound data,the processing of ultrasound data, the conversion of ultrasound data,the transmission of ultrasound data or images to a display device 30,the detection of operator inputs to the scanner 12, and/or the switchingof the settings of the scanner 12.

Also stored in the computer readable memory 16 may be computer readabledata 20, which may be used by the processor 14 in conjunction with thecomputer readable instructions 18 to provide the functions of the system10. Computer readable data 20 may include, for example, configurationsettings for the scanner 12, such as presets that instruct the processor14 how to collect and process the ultrasound data for a given body part.Such a preset may be selected, for example, depending on the processingof a control data frame against an AI model that is stored in thecomputer readable data 20. A preset may include numerous differentparameters for the scanner 12.

The scanner 12 includes a communications module 22 connected to theprocessor 14. In the illustrated example, the communications module 22wirelessly transmits signals to and receives signals from the displaydevice 30 along wireless communication link 24. The protocol used forcommunications between the scanner 12 and the display device 30 may beWiFi™ or Bluetooth™, for example, or any other suitable two-way radiocommunications protocol. The scanner 12 may operate as a WiFi™ hotspot,for example. Communication link 24 may use any suitable wireless networkconnection. In some embodiments, the communication link between thescanner 12 and the display device 30 may be wired. For example, thescanner 12 may be attached to a cord that may be pluggable into aphysical port of the display device 30.

The display device 30 may be, for example, a laptop computer, a tabletcomputer, a desktop computer, a smart phone, a smart watch, spectacleswith a built-in display, a television, a bespoke display or any otherdisplay device that is capable of being communicably connected to thescanner 12. The display device 30 may host a screen 32 and may include aprocessor 34, which is connected to a non-transitory computer readablememory 36 storing computer readable instructions 38, which, whenexecuted by the processor 34, cause the display device 30 to provide oneor more of the functions of the system 10. Such functions may be, forexample, the receiving of ultrasound data that may or may not bepre-processed; scan conversion of ultrasound data that is received intoa ultrasound images; processing of ultrasound data in control dataframes and/or image data frames; the display of an ultrasound image onthe screen 32; the display of a user interface; the control of thescanner 12; and/or the storage, application, reinforcing and/or trainingof an AI model.

Also stored in the computer readable memory 36 may be computer readabledata 40, which may be used by the processor 34 in conjunction with thecomputer readable instructions 38 to provide the functions of the system10. Computer readable data 40 may include, for example, settings for thescanner 12, such as presets for acquiring ultrasound data depending onthe analysis of control data frames; settings for a user interfacedisplayed on the screen 32; and/or one or more AI models. Settings mayalso include any other data that is specific to the way that the scanner12 operates or that the display device 30 operates.

It can therefore be understood that the computer readable instructionsand data used for controlling the system 10 may be located either in thecomputer readable memory 16 of the scanner 12, the computer readablememory 36 of the display device 30, and/or both the computer readablememories 16, 36.

The display device 30 may also include a communications module 42connected to the processor 34 for facilitating communication with thescanner 12. In the illustrated example, the communications module 42wirelessly transmits signals to and receives signals from the scanner 12on wireless communication link 24. However, as noted, in someembodiments, the connection between scanner 12 and display device 30 maybe wired.

Referring to FIG. 2, shown there generally is a schematic diagramshowing a series of control and image frames and their analysis,according to an embodiment of the present invention. In an embodiment,an ultrasound image feed is acquired by obtaining ultrasound data framesthat are converted to viewable image frames. During acquisition of theultrasound image feed, additional ultrasound control data frames may beacquired that are interspersed amongst the ultrasound data frames. Theultrasound control data frames may use reference scan parameters thatare consistent, regardless of whatever preset or settings the ultrasoundscanner is set to. The ultrasound control data frames may not beconverted to image frames for display, and instead are used to controlthe settings of the ultrasound scanner. This is illustrated in FIG. 2.

Furthermore, the acquisition of the ultrasound control data frames maynot necessarily interrupt the regular refresh rate of the displayedultrasound image feed.

In some cases, ultrasound data frames that are acquired are converted tooptimized viewable image frames, which are processed against anadditional AI model that identifies anatomical features in the optimizedviewable image frames. These features are then highlighted on thedisplayed, optimized image frames.

In FIG. 2, consecutive image data frames 50, 51, 52 are shown, followedby a control data frame 53, which in turn is followed by two furtherimage data frames 54, 55. The image data frames 50-55 are illustrated asa series of vertical scan lines that represent the image data that maybe acquired by the scanner 12. In the example, the image data frames50-55 are also shown as pre scan-converted scan lines that, for example,have not yet been converted to the reflect a curvature of the transducerarray of the ultrasound scanner 12. Image data frame 50 is acquiredusing a first set of parameters, for example parameters that are set bypreset P1. Image data frame 50 may then be scan converted intoultrasound image frame 60 for viewing. Likewise, image data frame 51 maybe acquired using the parameters for preset P1 and can also be scanconverted into ultrasound image frame 61 for viewing. Image data frame52 is also acquired using the parameters for preset P1 and convertedinto ultrasound image frame 62 for viewing. Preset P1 is, in thisexample, not optimal for the acquisition of ultrasound data for the bodypart 66 displayed in ultrasound image frames 60, 61, 62.

After the acquisition of a number of image data frames 50, 51, 52, acontrol data frame 53 may be acquired. The control data frame 53 may usereference parameters RP for acquiring the ultrasound data in the controldata frame 53. In general, the reference parameters RP may be configuredto be consistent, regardless of whatever preset or settings theultrasound scanner is set to. This may mean that the referenceparameters are different from the parameters of the preset P1 and theother presets that the scanner 12 may be capable of using. For example,as illustrated, the reference parameters used to acquire the controldata frame 53 have a shallower depth of scan than the parameters forpreset P1 used in the data frames 50, 51, 52. Also, there are fewer datalines (e.g., less line density, as shown via the sparser vertical scanlines) in the control data frame 53 than the parameters for preset P1used in the image data frames 50, 51, 52.

The control data frame 53 may not be converted to an image frame, butinstead is input to an AI model 70 for processing. The AI model 70 maybe provided on the scanner 12, the display device 30 and/or a serverthat is accessible to either the scanner 12 or the display device 30.The result of the processing against the AI model 70 is the predictionof a preset that is most suitable for the body part 66 that is beingscanned. In the example of FIG. 2, processing the control data frame 53with the AI model results in a prediction that the control data frame 53corresponds to a scan of a heart. As a result of this prediction, the AImodel 70 may output an instruction to the scanner 12 to set a preset P2(e.g., a cardiac preset) that is optimized for scanning a heart 66. Itcan be seen that the parameters for P2 (e.g. as illustrated, the depthand density of the vertical scan lines) are different from theparameters for preset P1. As illustrated, they are also different fromreference parameters RP for the control data frame 53.

While not illustrated, other example parameters that may be different indifferent presets are the ultrasound energy and/or the average powerusage of the scanner 12. For example, it may be the case that the AImodel 70 predicts the suitable preset for the control data frame 53 isan obstetrics/gynecology preset or an ocular preset, meaning that thecontrol data frames 53 may be scanning fetal tissue or eye tissue. Sincethese presets are generally also associated with limiting the poweroutput of the ultrasound energy used during scanning to enhance safetyfor more sensitive tissue types, changing to the preset by the AI model70 may also involve lower the ultrasound energy used for acquisition. Inthis manner, the embodiments described herein may provide enhancedsafety measures, in addition to providing enhancements to operatorworkflow that reduce the manual step of selecting a preset.

After having switched the scanner 12 to use the preset P2 predicted bythe AI model 70, the scanner 12 may continuing acquiring image dataframes 54, 55 in sequence using parameters for preset P2. These imagedata frames 54, 55 may then be scan converted to ultrasound image frames64, 65 respectively for viewing. As illustrated, the image frames 64, 65acquired using preset P2 may be better optimized for viewing of theimaged anatomy (e.g., when acquiring images using the cardiac preset,the heart is shown more fully in view with clearer lines) as compared toimage frames 60, 61, 62 acquired using preset P1.

To predict the settings that would suitable for a new control data frame53, the AI model 70 may previously be generated using machine learningmethods. For example, this may involve training the AI model with one ormore datasets containing different classes of control data frames thathave been labeled as being associated with various presets P1, P2, P3,P4 (shown in FIG. 2 with various label icons). These various presets maygenerally correspond to different types of anatomical features 74, 78,82, 86. For example, the preset P1 may generally be for scanning lungs74, preset P2 may generally be for scanning cardiac features 78, presetP3 may generally be for scanning bladders 82 , and preset P4 maygenerally be for scanning abdominal features such as kidneys 86 orlivers 88. In FIG. 2, the anatomical features 74, 78, 82, 86, 88 thatthe various presets P1-P4 are respectively associated with are shown indotted outline for illustrative purposes to provide a pictorialrepresentation of the anatomical features; but such pictorialrepresentations are not viewable ultrasound image frames.

The embodiments herein may generally involve using the manually-selectedpreset under which an ultrasound data frame or an ultrasound image frameis acquired to train an AI model to predict the preset that would besuitable for a new ultrasound data frame or ultrasound image frame.While using such data to train an AI model may be workable, it isrecognized there may be a mismatch between the training data (which werealready manually-selected to be acquired under an optimal preset) andthe new data that the AI model is to provide a prediction on (which maybe acquired under a set of different unknown parameters). This mismatchmay reduce the reliability of the predictions made by the AI model.

To improve the reliability of the AI model, in some embodiments,consistent reference parameters RP may be used to acquire the trainingdata that is labeled and inputted into the AI model, as well as the newdata that AI model is to predict the preset for. For example, this isshown in FIG. 2 where the various classes of the control data frames 72,76, 80, 84 in the one or more datasets may be acquired using consistentreference parameters RP (which are generally similar to the referenceparameters RP used to acquire the new control data frame 53 that the AImodel 70 is to predict a preset for).

Notably, the consistent reference parameters RP are used even though thedifferent presets P1-P4 (as associated with the different anatomicalfeatures 74, 78, 82, 86, 88) typically have their own associateddifferent optimal parameters. By configuring both the training controldata frames 72, 76, 80, 84 and a new control data frame 53 to beacquired using consistent reference parameters RP, there is no mismatchbetween the acquisition parameters of the training data and the data forwhich the AI model is to provide a prediction on. This may enhance theoperation of the machine learning algorithms so that the AI model 70 cangenerate more reliable predictions about the preset that is suitable fora given new control data frame. This may also allow certain steps ofnormalizing the training data (e.g., for scan depth, resolution,contrast, brightness, image enhancements, noise reduction and the like)to be minimized. In various embodiments, the consistent referenceparameters RP may have one or more of: a fixed depth, a fixed number ofacquisition lines, fixed focal zones, a fixed sampling rate, fixed gain,fixed beamformer parameters, or fixed application of filters.

As shown in FIG. 2, the training is performed on control data frames 72,76, 80, 84 similar to how a control data frame 53 (as opposed to aviewable ultrasound image frame) is fed into the AI model 70 forprediction. By performing the machine learning on pre-scan converteddata frames 72, 76, 80, 84, 53, the act of scan converting control dataframes into viewable ultrasound image frames may be avoided. This mayenhance computational efficiency by reducing the computational effortrequired to perform scan conversion. Also, since pre-scan convertedcontrol data frames generally have a smaller memory footprint thanpost-scan converted ultrasound image frames, this may allow forincreased throughput of various machine learning processes.

Notwithstanding, it is not required that the machine learning techniquesdescribed herein be performed on pre-scan converted data. In someembodiments, the methods described herein may be performed on post-scanconverted images (e.g., on image data after the control data frames 53,72, 76, 80, 84 are scan converted). In various embodiments, data frames(whether control data frames 72, 76, 80, 84 or scan converted image dataframes) used as training data for the AI model 70 may include greyscaledata. In various embodiments, these data frames used for training mayalso include Doppler data.

In various embodiments, the training control data frames 72, 76, 80, 84may be acquired using the same model of ultrasound scanner 12 on whichthe AI model 70 will be deployed. However, in some embodiments, themodel of ultrasound scanner used to acquire the training control dataframes 72, 76, 80, 84 may differ from the model of ultrasound scanner 12on which the AI model 70 is deployed (e.g., different manufacturer ordesign). This may be possible because consistent reference parameters RPare used for both the training control data frames 72, 76, 80, 84 andthe new control data frame 53.

In some embodiments, the different models of ultrasound scanners 12 mayeven have different transducer array footprints (e.g., linear,curvilinear, or microconvex, or phased array) and/or different frequencyranges. This may be accomplished by configuring the control data frame53 to use reference parameters RP that only acquire data lines from acenter portion of the transducer array that are common to all transducerarray footprint types, and by selecting an imaging frequency and depththat is common or overlaps amongst the different scanner types. Sincethe machine learning may be performed on pre-scan converted control dataframes so that do not reflect any curvature of the transducer arrayfootprint, using consistent reference parameters RP may allow thecontrol data frame 53 acquired by the various scanner types to shareenough common characteristics, so as to allow their variousclassifications to be applicable to other scanner models.

By controlling the parameters in this manner, the generated AI model 70may be sufficiently robust to predict presets for a new control dataframe 53 acquired from a scanner model 12 that is different from thatwhich is used to acquire the training control data frames 72, 76, 80,84.

As illustrated in FIG. 2, the AI model 70 may be trained with classes ofcontrol data frames 72, 76, 80, 84 that correspond to presets P1-P4 forscanning a single type of anatomy (e.g., for lungs 74, cardiac 78, orbladders 82), or multiple types of anatomy (e.g., an abdomen presetwhich may suitable for scanning kidneys 86 and livers 88).

In various embodiments, the different classes of ultrasound control dataframes used for the AI model may generally include ultrasound dataacquired for one or more anatomical features; such anatomical featuresincluding a lung, a heart, a liver, a kidney, a bladder, an eye, a womb,a thyroid gland, a breast, a brain, an artery, a vein, a muscle, anembryo, a tendon, a bone, a fetus, a prostate, a uterus, an ovary,testes, a pancreas, or a gall bladder.

In various embodiments, the different presets for which there may belabeled training control data frames may generally include presets forat least two of abdomen, cardiac, bladder, lung, obstetrics/gynecology,transcranial, superficial, thyroid, vascular, musculoskeletal, breast,ocular, prostate, fertility, or nerve.

Referring still to FIG. 2, the control data frames 53 that are collectedare interspersed amongst the image data frames 50, 51, 52, 54, 55, andthey may continue to be acquired in an interspersed fashion as theultrasound scan proceeds. For example, there may be one control dataframe 53 acquired for every three image data frames 50, 51, 52. Otherinterspersion rates are possible, and an interspersion rate may changeduring use of the scanner 12 (e.g., in some embodiments, control dataframes 53 may be interleaved with image data frames 50, 51, 52, 54, 54).

The acquisition of the control data frames 53 may be configured to haveminimal impact on the refresh rate of the image frames 60, 61, 62, 64,65. In part this is because the processing of the control data frame 53with the AI model 70 is performed on pre-scan converted image data,before being scan converted and placed into the image buffer. As aresult, the image buffer may only have (not necessarily at the sametime) image frames 60, 61, 62, 64, 65 that are going to be displayed, atregular intervals, so that the image refresh rate is uniform and theimage feed appears smooth, without the acquisition and processing of thecontrol data frames 53 causing any significant pause or interruption.The average acquisition rate of the image data frames, however, shouldbe about equal to the refresh rate of the image that is displayed sothat there is always enough data in the image buffer that the imagerefresh rate is not interrupted.

In various embodiments, the consistent reference parameters RP may beoptimized to sufficiently allow for the machine learning processesdescribed herein, while reducing the impact of the control data frames53 on the image quality of ultrasound image frames 60, 61, 62, 64, 65.For example, the various parameters of the reference parameters RP maybe configured to consume less resources (e.g., fewer lines, focal zones,or the like) so as to reduce their acquisition time, and their potentialnegative impact on frame rate.

Additionally or alternatively, in some embodiments, the referenceparameters RP can be configured to minimize acquisition time of thecontrol data frame 53 (e.g., by reducing the number of acquisition linesin a control data frame 53 versus the number of acquisition lines in animage data frame 50, 51, 52, 54, 55)

In some embodiments, during operation of the scanner 12, the user may bepresented with a set of presets and an auto-preset option. While eachpreset is suited for a particular part of the anatomy, the auto-presetoption, if selected, will automatically predict and select the optimumpreset using the AI model 70 as described herein.

In some embodiments, the capture and analysis of the control data frame53 may occur fast enough for the settings of the scanner 12 to bechanged in time for the acquisition of the immediately following imagedata frame 54.

In some cases, the preset on the scanner 12 may be changed based on thepredicted preset for a single control data frame 53. However, in someembodiments, the preset on the scanner 12 may be changed only aftermultiple control data frames 53 result in the same predicted preset. Inthis latter scenario, different configurations are possible. Forexample, it may be required that some consecutive number of control dataframes 53 predict the same preset prior to the scanner 12 changing itspreset. In another embodiment, the scanner 12 may change its presetafter some percentage (e.g., 60-99%) of a past number of control dataframes 53 provide the same predicted preset form the AI model 70.

Referring to FIG. 3, a flowchart shows an exemplary process undertakenby the system 10 (as shown in FIG. 1), in which the scanner settings areupdated as a result of analysis of a control data frame 53. Indiscussing FIG. 3, reference will also be generally made to the sequenceof various frames shown in FIG. 2. In step 100, an image data framecounter is set to zero (i=0). In step 102, an image data frame 50 isacquired using whatever the current settings of the scanner 12 are. Forexample, the settings may be default settings, a particular preset, orsettings that have been made manually. In step 104, the data in theimage data frame 50 may be scan converted into a form suitable fordisplay, following which the image frame 60 may be displayed in step106. After the display of the image frame 60, the image data framecounter may be incremented in step 108. In step 110, the current value iof the data frame counter may be compared to a limit value n. If thevalue of the data frame counter is not yet equal to the limit value n,then the process reverts to step 102 in which another image data frame51 is acquired. Steps 102-110 may be repeated, with subsequent imagedata frames being acquired, scan converted and displayed until the imagedata frame counter equals the limit value (i=n) in step 110.

When a series of n image data frames have been acquired (i=n), theprocess moves onto step 112, in which a control data frame 53 isacquired. The control data frame 53 may be acquired using the referencescan parameters, or reference settings, which are typically not the sameas whatever the current settings were for step 102. In step 114, thecontrol data present in the control data frame 53 may be analyzed, forexample by processing it against the AI model 70. As a result of thisprocessing, the AI model 70 may predict, in step 116, the optimalsettings for the scanner 12 for the particular body part that iscurrently being scanned. In step 118, the settings of the scanner 12 maybe updated. Updating the settings of the scanner 12 may entail changingfrom one preset to another preset (e.g., changing the settings from onevalue or set of values to another value or set of values). In othercases, updating the settings of the scanner 12 may entail changing theexisting settings of the scanner 12 to those of a preset. In still othercases, the updating of the scanner settings may be to confirm that thepresent settings are already optimal and do not yet need to be changed.

After the scanner settings have been updated in step 118, which may ormay not involve an actual change of the settings, the process may repeatfrom step 100.

Referring to FIG. 4, a flowchart is shown for training the AI model 70.When discussing FIG. 4 below, reference will also be made to certainelements of FIGS. 1 and 2. In step 130, a preset of the scanner 12 ismanually selected according to a type of body part that is to bescanned. In step 132, the scanner 12 acquires image data frames (e.g.54, 55) and control data frames 53 that may be interspersed amongst theimage data frames. The control data frames 53 are then labeled, in step134, as being associated with the manually selected preset. The labeledcontrol data frames may then be sent to a server, in step 136, wherethey are saved as training control data frames (e.g., control dataframes 72, 76, 80, 84 as shown in FIG. 2). These training control dataframes made be accessible to the AI model 70 for further training and/orre-enforcement. In step 138, the AI model 70 may be trained using thelabeled control data frames that are stored in the server.

A common challenge in machine learning activities is obtaining labeleddata that can be used to train an AI model 70. For example, if using asupervised learning technique, traditionally, human involvement may beneeded to label which control data frames should be associated withwhich presets to generate suitable dataset that can be used to train theAI model. Such manual review and labeling is laborious, so as to make itdifficult to create a large and robust dataset. However, by applying themethod of FIG. 4 and inserting a control data frame 53 into the regularscanning activity performed by operators when using a scanner 12, themanual selection of the preset by the operators may be used as the humandata labeling activity. For example, the method of FIG. 4 may bedeployed on scanners 12 that do not have the AI model 70 enabled, so asto collect training data based on the presets selected by the operator.Then, once sufficient training data has been obtained and the AI model70 trained, the AI model 70 may be deployed to enable the AImodel-enabled preset prediction methods described herein.

Referring to FIG. 5, a flowchart is shown for using and reinforcing theAI model 70. In discussing FIG. 5, reference will again also be made tothe elements of FIG. 2. In some embodiments, during continued use of theultrasound scanner 12 after a preset P2 has been predicted by the AImodel 70 based on control data frame 53, subsequently acquiredultrasound control data frames may be used for further training orreinforcement of the AI model 70.

In step 150 of FIG. 5, the control data in a control data frame 53 maybe processed against the AI model 70. In step 152, the AI model 70predicts the optimal preset, and the preset of the scanner 12 may bechanged in step 154. On a continuing basis, the control data acquired insubsequent interspersed control data frames is monitored may be step156, by processing it against the AI model 70. For example, in theexample of FIG. 2, acts 150-154 may be performed on a control data frame53 against AI model 70. Referring back to FIG. 5, if, in step 158, aftera period of time, the subsequent control data frames still correspond tothe changed preset, then the subsequently obtained control data framescan be labeled, in step 160, as corresponding to the preset changed toin step 154. In step 162, the labeled control data frames may then besent for storage in a location that is accessible by the AI model 70 forfurther training or reinforcement.

In some embodiments, in the auto-preset mode, the user interface on thedisplay device 30 may be configured to show the new preset each time thepreset is changed, and display an option for the user to cancel thechange for a few seconds after the change. If used, the cancellation maybe considered to be training data for the AI model 70. In the case ofcancellation, the control data frames collected after the cancellationmay be labeled as not corresponding to the preset predicted by the AImodel 70 and sent to the server. If, within some period of time, a usermanually selects a different preset after cancellation and the controldata frames 53 continue to be similar to what was being acquired priorto cancellation, then the user-selected preset may also serve astraining data for the AI model 70.

For example, it may be the case that the class of cardiac preset-relateddata in the original dataset used to train the AI model 70 lacks afour-chamber cardiac view, such that if control data frames for such animage is acquired for the first time during scanning, the AI model 70fails to accurately predict use of the cardiac preset. If the usermanually selects the cardiac preset while control data frames for suchan image is being acquired (e.g., after cancelling selecting of a presetpredicted by the AI model 70), then the control data frames for thefour-chamber cardiac view may be added as training data so that the AImodel 70 may learn that such control data frames are associated with acardiac preset.

Referring to FIG. 6, a flowchart is shown for identifying particularfeatures in an ultrasound image 64, 65. In discussing FIG. 6, referencewill also be made to the elements of FIGS. 1 and 2. Once the AI model 70has predicted the optimal preset for the scanner 12, and the scanner 12has been set to the predicted preset, then it may be possible to analyzethe image frame 64, 65 that is used for viewing by an operator of thescanner 12. This further analysis may use an additional, separate AImodel, which may be referred to as a micro-AI model, with the AI model70 being referred to as a macro-AI model. The system 10 therefore mayuse two different AI models simultaneously, each applied to a differentlevel of the ultrasound image acquisition process, the first beingapplied to control data frames 53 and the second being applied to imageframes 64, 65 after the settings have been optimized using the first AImodel.

In step 180 (which may, for example, be equivalent to multiple instancesof step 102 in the method of FIG. 2 after cycling through steps 118 or100), the system 10 may continue to acquire image data frames 54, 55after the preset of the scanner 12 has been updated. As each image dataframe is acquired, the scanner 12 may scan convert the image data framesto viewable image frames 64. 65, in step 182. The viewable image frames64, 65 may then be processed against a second AI model (step 184) thatis trained to identify (e.g., segment) anatomical features in the imageframes 64, 65. In step 186, the second AI model may identify one or moreanatomical features in the image frames 64, 65. The image frames 64, 65may then be displayed with additional highlights, in step 190, to showwhere the identified anatomical features are in the images. Optionally,the highlighted areas may be annotated with the name of the anatomicalfeature or features that have been identified.

Referring to FIG. 7, a system 200 is shown in which there are multiplesimilar or different scanners 12, 202, 204 connected to theircorresponding display devices 30, 206, 208 and either connecteddirectly, or indirectly via the display devices, to a network 210, suchas the internet. The scanners 12, 202, 204 may be connected onwards viathe network 210 to a server 220.

The server 220 may include a processor 222, which may be connected to anon-transitory computer readable memory 224 storing computer readableinstructions 226, which, when executed by the processor 222, cause theserver 220 to provide one or more of the functions of the system 200.Such functions may be, for example, the receiving of ultrasound datathat may or may not be pre-processed, the scan conversion of ultrasounddata that is received into an ultrasound image, the processing ofultrasound data in control data frames or image data frames, the controlof the scanners 12, 202, 204, and/or machine learning activities relatedto one or more AI models. Such machine learning activities may includethe training and/or reinforcing of one or more AI models.

Also stored in the computer readable memory 224 may be computer readabledata 228, which may be used by the processor 222 in conjunction with thecomputer readable instructions 226 to provide the functions of thesystem 200. Computer readable data 228 may include, for example,settings for the scanners 12, 202, 204 such as preset parameters foracquiring ultrasound data depending on the analysis of control dataframes, settings for user interfaces displayed on the display devices30, 206, 208, and one or more AI models. For example, one AI model maybe the AI model 70 that is used to analyze the control data frames 53,while another AI model may be used to analyze image frames 64, 65 foridentifying anatomical features in the image frames 64, 65. Settings mayalso include any other data that is specific to the way that thescanners 12, 202, 204 operate or that the display devices 30, 206, 208operate.

It can therefore be understood that the computer readable instructionsand data used for controlling the system 200 may be located either inthe computer readable memory of the scanners 12, 202, 204, the computerreadable memory of the display devices 30, 206, 208, the computerreadable memory 224 of the server 220, or any combination of theforegoing locations.

As noted above, even though the scanners 12, 202, 204 may be different,the control data frames that are captured by them are all captured withconsistent reference parameters RP, so that each control data frameacquired may be used by the AI model 70 for training, without anyspecial pre-processing of the captured data. Likewise, the control dataframes acquired by the individual scanners 12, 202, 204 may all beprocessed against the AI model 70 directly for prediction of the optimalpresets and/or for reinforcement of the AI model 70.

In some embodiments, AI models 70 present in the scanner 12 may beupdated from time to time from an AI model present in the server 220.

In some embodiments, the analysis of the control data frames may beperformed using a rules-based engine rather than an AI model.

Embodiments of the invention may be implemented using specificallydesigned hardware, configurable hardware, programmable data processorsconfigured by the provision of software (which may optionally include‘firmware’) capable of executing on the data processors, special purposecomputers or data processors that are specifically programmed,configured, or constructed to perform one or more steps in a method asexplained in detail herein and/or combinations of two or more of these.Examples of specifically designed hardware are: logic circuits,application-specific integrated circuits (“ASICs”), large scaleintegrated circuits (“LSIs”), very large scale integrated circuits(“VLSIs”) and the like. Examples of configurable hardware are: one ormore programmable logic devices such as programmable array logic(“PALs”), programmable logic arrays (“PLAs”) and field programmable gatearrays (“FPGAs”). Examples of programmable data processors are:microprocessors, digital signal processors (“DSPs”), embeddedprocessors, graphics processors, math co-processors, general purposecomputers, server computers, cloud computers, main computers, computerworkstations, and the like. For example, one or more data processors ina control circuit for a device may implement methods as described hereinby executing software instructions in a program memory accessible to theprocessors.

While processes or blocks are presented in a given order, alternativeexamples may perform routines having steps, or employ systems havingblocks, in a different order, and some processes or blocks may bedeleted, moved, added, subdivided, combined, and/or modified to providealternative or subcombinations. Each of these processes or blocks may beimplemented in a variety of different ways. Also, while processes orblocks are at times shown as being performed in series, these processesor blocks may instead be performed in parallel, or may be performed atdifferent times.

The embodiments may also be provided in the form of a program product.The program product may include any non-transitory medium which carriesa set of computer-readable instructions which, when executed by a dataprocessor, cause the data processor to execute a method of theinvention. Program products according to the invention may be in any ofa wide variety of forms. The program product may include, for example,non-transitory media such as magnetic data storage media includingfloppy diskettes, hard disk drives, optical data storage media includingCD ROMs, DVDs, electronic data storage media including ROMs, flash RAM,EPROMs, hardwired or preprogrammed chips (e.g., EEPROM semiconductorchips), nanotechnology memory, or the like. The computer-readablesignals on the program product may optionally be compressed orencrypted.

Where a component (e.g. software, processor, support assembly, valvedevice, circuit, etc.) is referred to above, unless otherwise indicated,reference to that component (including a reference to a “means”) shouldbe interpreted as including as equivalents of that component anycomponent which performs the function of the described component (i.e.,that is functionally equivalent), including components which are notstructurally equivalent to the disclosed structure which performs thefunction in the illustrated exemplary embodiments of the invention.

Specific examples of systems, methods and apparatus have been describedherein for purposes of illustration. These are only examples. Thetechnology provided herein can be applied to systems other than theexample systems described above. Many alterations, modifications,additions, omissions and permutations are possible within the practiceof this invention. This invention includes variations on describedembodiments that would be apparent to the skilled addressee, includingvariations obtained by: replacing features, elements and/or acts withequivalent features, elements and/or acts; mixing and matching offeatures, elements and/or acts from different embodiments; combiningfeatures, elements and/or acts from embodiments as described herein withfeatures, elements and/or acts of other technology; and/or omittingcombining features, elements and/or acts from described embodiments. Insome embodiments, the components of the systems and apparatuses may beintegrated or separated. Moreover, the operations of the systems andapparatuses disclosed herein may be performed by more, fewer, or othercomponents and the methods described may include more, fewer, or othersteps. In other instances, well known elements have not been shown ordescribed in detail and repetitions of steps and features have beenomitted to avoid unnecessarily obscuring the invention. Screen shots mayshow more or less than the examples given herein. Accordingly, thespecification is to be regarded in an illustrative, rather than arestrictive, sense.

It is therefore intended that the appended claims and claims hereafterintroduced are interpreted to include all such modifications,permutations, additions, omissions and sub-combinations as mayreasonably be inferred. The scope of the claims should not be limited bythe embodiments set forth in the examples but should be given thebroadest interpretation consistent with the description as a whole.

C. Interpretation of Terms

Unless the context clearly requires otherwise, throughout thedescription and the claims, the following applies:

In general, unless otherwise indicated, singular elements may be in theplural and vice versa with no loss of generality. The use of themasculine can refer to masculine, feminine or both.

The terms “comprise”, “comprising” and the like are to be construed inan inclusive sense, as opposed to an exclusive or exhaustive sense, thatis to say, in the sense of “including, but not limited to”.

The terms “connected”, “coupled”, or any variant thereof, means anyconnection or coupling, either direct or indirect, between two or moreelements; the coupling or connection between the elements can bephysical, logical, or a combination thereof.

The words “herein,” “above,” “below” and words of similar import, whenused in this application, refer to this application as a whole and notto any particular portions of this application.

The word “or” in reference to a list of two or more items covers all ofthe following interpretations of the word: any of the items in the list,all of the items in the list and any combination of the items in thelist.

Words that indicate directions such as “vertical”, “transverse”,“horizontal”, “upward”, “downward”, “forward”, “backward”, “inward”,“outward”, “vertical”, “transverse”, “left”, “right”, “front”, “back”,“top”, “bottom”, “below”, “above”, “under”, and the like, used in thisdescription and any accompanying claims (where present) depend on thespecific orientation of the apparatus described and illustrated. Thesubject matter described herein may assume various alternativeorientations. Accordingly, these directional terms are not strictlydefined and should not be interpreted narrowly.

To aid the Patent Office and any readers of any patent issued on thisapplication in interpreting the claims appended hereto, applicant wishesto note that they do not intend any of the appended claims or claimelements to invoke 35 U.S.C. 112(f) unless the words “means for” or“step for” are explicitly used in the particular claim.

D. Claim Support

Disclosed herein is a method for controlling settings of an ultrasoundscanner, the method comprising: acquiring an ultrasound image feed bysequentially obtaining ultrasound data frames that are converted toviewable image frames; and during acquisition of the ultrasound imagefeed: acquiring, using reference scan parameters, ultrasound controldata frames that are interspersed amongst the ultrasound data frames,the reference scan parameters being consistently used for theinterspersed ultrasound control data frames regardless of scanparameters that are used for acquiring the ultrasound image feed; andusing the ultrasound control data frames to control the settings of theultrasound scanner.

In some embodiments, the ultrasound control data frames are notconverted to viewable image frames.

In some embodiments, the reference scan parameters consistently used forthe interspersed ultrasound control data frames have one of: a fixeddepth, a fixed number of acquisition lines, fixed focal zones or a fixedsampling rate.

In some embodiments, the acquiring the ultrasound image feed isperformed according to a first preset, and the control of the settingsof the ultrasound scanner comprises changing the first preset to asecond preset different from the first preset.

In some embodiments, the ultrasound scanner outputs first ultrasoundenergy when operating according to the first preset, and the ultrasoundscanner outputs second ultrasound energy when operating according to thesecond preset, and the second ultrasound energy has a lower power levelthan the first ultrasound energy.

In some embodiments, prior to changing the first preset to the secondpreset, at least the latest of the ultrasound control data frames isprocessed against an artificial intelligence model to predict a suitablepreset for the ultrasound image feed, and the predicted suitable presetis used as the second preset that the ultrasound scanner is changed to.

In some embodiments, the artificial intelligence model is trained withone or more datasets containing different classes of ultrasound controldata frames for different presets, and substantially all the ultrasoundcontrol data frames in the one or more datasets are consistentlyacquired using the reference scan parameters.

In some embodiments, the different presets comprise presets for at leasttwo of abdomen, cardiac, bladder, lung, obstetrics/gynecology,transcranial, superficial, thyroid, vascular, musculoskeletal, breast,ocular, prostate, fertility, or nerve.

In some embodiments, the different classes of ultrasound control dataframes comprise ultrasound data acquired of different body parts, thedifferent body parts comprising at least two of: a lung, a heart, aliver, a kidney, a bladder, an eye, a womb, a thyroid gland, a breast, abrain, an artery, a vein, a muscle, an embryo, a tendon, a bone, afetus, a prostate, a uterus, an ovary, testes, a pancreas, or a gallbladder.

In some embodiments, after changing from the first preset to the secondpreset, the method further comprises: acquiring additional ultrasounddata frames according the second preset, the additional ultrasound dataframes being converted to optimized viewable image frames; processingthe optimized viewable image frames against an additional artificialintelligence model that identifies anatomical features in the optimizedviewable image frames; and displaying the optimized viewable imageframes with the anatomical features, as identified by the additionalartificial intelligence model, highlighted.

In some embodiments, the method further comprises: monitoringsubsequently acquired ultrasound control data frames to determine if thesubsequently acquired ultrasound control data frames continue tocorrespond to the second preset; and after a period of time, labelingthe subsequently acquired ultrasound control data frames as ultrasoundcontrol data frames that correspond to the second preset, so that thesubsequently acquired ultrasound control data frames can used fortraining or reinforcing an artificial intelligence model.

Also disclosed herein is a method of labeling ultrasound images forinput into an artificial intelligence model, comprising: operating anultrasound scanner according to a user-selected preset; acquiring anultrasound image feed by obtaining ultrasound data frames based on theuser-selected preset; during acquisition of the ultrasound image feed,acquiring, using reference scan parameters, ultrasound control dataframes that are interspersed amongst the ultrasound data frames, thereference scan parameters being consistently used for the interspersedultrasound control data frames regardless of scan parameters that aredefined for the user-selected preset; labeling the ultrasound controldata frames as corresponding to the user-selected preset; and sendingthe labeled ultrasound control data frames to a server for adding to adataset, wherein the dataset can be used to train the artificialintelligence model for predicting whether the user-selected preset wouldbe suitable for later-acquired ultrasound control data frames.

Also disclosed herein is an ultrasound scanner that controls itssettings, the ultrasound scanner comprising a processor and computerreadable memory storing computer readable instructions, which, whenexecuted by the processor, cause the ultrasound scanner to: acquire anultrasound image feed by sequentially obtaining ultrasound data framesthat are converted to viewable image frames; and during acquisition ofthe ultrasound image feed: acquire, using reference scan parameters,ultrasound control data frames that are interspersed amongst theultrasound data frames, the reference scan parameters being consistentlyused for the interspersed ultrasound control data frames regardless ofscan parameters that are used for acquiring the ultrasound image feed;and use the ultrasound control data frames to control the settings ofthe ultrasound scanner.

In some embodiments of the ultrasound scanner, the ultrasound controldata frames are not converted to viewable image frames.

In some embodiments of the ultrasound scanner, the reference scanparameters consistently used for the interspersed ultrasound controldata frames have one of: a fixed depth, a fixed number of acquisitionlines, fixed focal zones or a fixed sampling rate.

In some embodiments the ultrasound scanner comprises: a first presetaccording to which the ultrasound image feed is acquired; and a secondpreset different from the first preset; wherein control of the settingsof the ultrasound scanner comprises changing the first preset to thesecond preset.

In some embodiments of the ultrasound scanner, the ultrasound scanneroutputs first ultrasound energy when operating according to the firstpreset, and the ultrasound scanner outputs second ultrasound energy whenoperating according to the second preset, and the second ultrasoundenergy has a lower power level than the first ultrasound energy.

In some embodiments the ultrasound scanner comprises an artificialintelligence model, wherein the ultrasound scanner is configured to:prior to changing the first preset to the second preset, process atleast the latest of the ultrasound control data frames against theartificial intelligence model to predict a suitable preset for theultrasound image feed; and use the predicted suitable preset as thesecond preset that the ultrasound scanner is changed to.

In some embodiments of the ultrasound scanner, the artificialintelligence model is trained with one or more datasets containingdifferent classes of ultrasound control data frames for differentpresets, and substantially all the ultrasound control data frames in theone or more datasets are consistently acquired using the reference scanparameters.

In some embodiments of the ultrasound scanner, the different presetscomprise presets for at least two of abdomen, cardiac, bladder, lung,obstetrics/gynecology, transcranial, superficial, thyroid, vascular,muscular, breast, ocular, prostate, fertility, or nerve.

In some embodiments of the ultrasound scanner, the different classes ofultrasound control data frames comprise ultrasound data acquired ofdifferent body parts, the different body parts comprising at least twoof: a lung, a heart, a liver, a kidney, a bladder, an eye, a womb, athyroid gland, a breast, a brain, an artery, a vein, a muscle, anembryo, a tendon, a bone, a fetus, a prostate, a uterus, an ovary,testes, a pancreas, or a gall bladder.

In some embodiments, the ultrasound scanner is further configured, afterchanging from the first preset to the second preset, to: acquireadditional ultrasound data frames according the second preset, theadditional ultrasound data frames being converted to optimized viewableimage frames; process the optimized viewable image frames against anadditional artificial intelligence model that identifies anatomicalfeatures in the optimized viewable image frames; and cause the optimizedviewable image frames to be displayed with the anatomical features, asidentified by the additional artificial intelligence model, highlighted.

In some embodiments, the ultrasound scanner is further configured to:monitor subsequently acquired ultrasound control data frames todetermine if the subsequently acquired ultrasound control data framescontinue to correspond to the second preset; and after a period of time,label the subsequently acquired ultrasound control data frames asultrasound control data frames that correspond to the second preset, sothat the subsequently acquired ultrasound control data frames can usedfor training or reinforcing an artificial intelligence model.

Also disclosed herein is an ultrasound scanner for labeling ultrasoundimages for input into an artificial intelligence model, comprising aprocessor and computer readable memory storing computer readableinstructions, which, when executed by the processor, cause theultrasound scanner to: operate according to a user-selected preset;acquire an ultrasound image feed by obtaining ultrasound data framesbased on the user-selected preset; during acquisition of the ultrasoundimage feed, acquire, using reference scan parameters, ultrasound controldata frames that are interspersed amongst the ultrasound data frames,the reference scan parameters being consistently used for theinterspersed ultrasound control data frames regardless of scanparameters that are defined for the user-selected preset; label theultrasound control data frames as corresponding to the user-selectedpreset; and send the labeled ultrasound control data frames to a serverfor adding to a dataset, wherein the dataset can be used to train theartificial intelligence model for predicting whether the user-selectedpreset would be suitable for later-acquired ultrasound control dataframes.

Also disclosed is a system for controlling the settings of an ultrasoundscanner; the system comprising: a server comprising an artificialintelligence model; an ultrasound scanner operably connected to theserver, the ultrasound scanner comprising a processor and computerreadable memory storing computer readable instructions, which, whenexecuted by the processor, cause the ultrasound scanner to: acquire anultrasound image feed by sequentially obtaining ultrasound data framesthat are converted to viewable image frames; and during acquisition ofthe ultrasound image feed: acquire, using reference scan parameters,ultrasound control data frames that are interspersed amongst theultrasound data frames, the reference scan parameters being consistentlyused for the interspersed ultrasound control data frames regardless ofscan parameters that are used for acquiring the ultrasound image feed;and process at least the latest of the ultrasound control data framesagainst the artificial intelligence model to predict a suitable presetfor the ultrasound image feed; and use the predicted suitable preset tocontrol the settings of the ultrasound scanner by changing theultrasound scanner from a first preset to a second preset.

In some embodiments of the system, the artificial intelligence model istrained with one or more datasets containing different classes ofultrasound control data frames for different presets, and substantiallyall the ultrasound control data frames in the one or more datasets areconsistently acquired using the reference scan parameters.

In some embodiments of the system, the different presets comprisepresets for at least two of abdomen, cardiac, bladder, lung,obstetrics/gynecology, transcranial, superficial, thyroid, vascular,muscular, breast, ocular, prostate, fertility, or nerve.

In some embodiments of the system, the different classes of ultrasoundcontrol data frames comprise ultrasound data acquired of different bodyparts, the different body parts comprising at least two of: a lung, aheart, a liver, a kidney, a bladder, an eye, a womb, a thyroid gland, abreast, a brain, an artery, a vein, a muscle, an embryo, a tendon, abone, a fetus, a prostate, a uterus, an ovary, testes, a pancreas, or agall bladder.

In some embodiments, the system comprised a display device operablyconnected to the ultrasound scanner, wherein: the ultrasound scanner isfurther configured, after changing from the first preset to the secondpreset, to acquire additional ultrasound data frames according thesecond preset, the additional ultrasound data frames being converted tooptimized viewable image frames; the server is configured to process theoptimized viewable image frames against an additional artificialintelligence model that identifies anatomical features in the optimizedviewable image frames; and the display device displays the optimizedviewable image frames with the anatomical features, as identified by theadditional artificial intelligence model, highlighted.

In some embodiments, the system is further configured to: monitorsubsequently acquired ultrasound control data frames to determine if thesubsequently acquired ultrasound control data frames continue tocorrespond to the second preset; and after a period of time, label thesubsequently acquired ultrasound control data frames as ultrasoundcontrol data frames that correspond to the second preset, so that thesubsequently acquired ultrasound control data frames can used fortraining or reinforcing the artificial intelligence model.

1. A method for controlling settings of an ultrasound scanner, themethod comprising: acquiring an ultrasound image feed by sequentiallyobtaining ultrasound data frames that are converted to viewable imageframes; and during acquisition of the ultrasound image feed: acquiring,using reference scan parameters, ultrasound control data frames that areinterspersed amongst the ultrasound data frames, the reference scanparameters being consistently used for the interspersed ultrasoundcontrol data frames regardless of scan parameters that are used foracquiring the ultrasound image feed; and using the ultrasound controldata frames to control the settings of the ultrasound scanner.
 2. Themethod of claim 1, wherein the ultrasound control data frames are notconverted to viewable image frames.
 3. The method of claim 1, whereinthe reference scan parameters consistently used for the interspersedultrasound control data frames have one of: a fixed depth, a fixednumber of acquisition lines, fixed focal zones or a fixed sampling rate.4. The method of claim 1, wherein the acquiring the ultrasound imagefeed is performed according to a first preset, and the control of thesettings of the ultrasound scanner comprises changing the first presetto a second preset different from the first preset.
 5. The method ofclaim 4, wherein the ultrasound scanner outputs first ultrasound energywhen operating according to the first preset, and the ultrasound scanneroutputs second ultrasound energy when operating according to the secondpreset, and the second ultrasound energy has a lower power level thanthe first ultrasound energy.
 6. The method of claim 4, wherein prior tochanging the first preset to the second preset, at least the latest ofthe ultrasound control data frames is processed against an artificialintelligence model to predict a suitable preset for the ultrasound imagefeed, and the predicted suitable preset is used as the second presetthat the ultrasound scanner is changed to.
 7. The method of claim 6,wherein the artificial intelligence model is trained with one or moredatasets containing different classes of ultrasound control data framesfor different presets, and substantially all the ultrasound control dataframes in the one or more datasets are consistently acquired using thereference scan parameters.
 8. The method of claim 7, wherein thedifferent presets comprise presets for at least two of abdomen, cardiac,bladder, lung, obstetrics/gynecology, transcranial, superficial,thyroid, vascular, musculoskeletal, breast, ocular, prostate, fertility,or nerve.
 9. The method of claim 7, wherein the different classes ofultrasound control data frames comprise ultrasound data acquired ofdifferent body parts, the different body parts comprising at least twoof: a lung, a heart, a liver, a kidney, a bladder, an eye, a womb, athyroid gland, a breast, a brain, an artery, a vein, a muscle, anembryo, a tendon, a bone, a fetus, a prostate, a uterus, an ovary,testes, a pancreas, or a gall bladder.
 10. The method of claim 6,wherein after changing from the first preset to the second preset, themethod further comprises: acquiring additional ultrasound data framesaccording the second preset, the additional ultrasound data frames beingconverted to optimized viewable image frames; processing the optimizedviewable image frames against an additional artificial intelligencemodel that identifies anatomical features in the optimized viewableimage frames; and displaying the optimized viewable image frames withthe anatomical features, as identified by the additional artificialintelligence model, highlighted.
 11. The method of claim 4, furthercomprising: monitoring subsequently acquired ultrasound control dataframes to determine if the subsequently acquired ultrasound control dataframes continue to correspond to the second preset; and after a periodof time, labeling the subsequently acquired ultrasound control dataframes as ultrasound control data frames that correspond to the secondpreset, so that the subsequently acquired ultrasound control data framescan used for training or reinforcing an artificial intelligence model.12. A method of labeling ultrasound images for input into an artificialintelligence model, comprising: operating an ultrasound scanneraccording to a user-selected preset; acquiring an ultrasound image feedby obtaining ultrasound data frames based on the user-selected preset;during acquisition of the ultrasound image feed, acquiring, usingreference scan parameters, ultrasound control data frames that areinterspersed amongst the ultrasound data frames, the reference scanparameters being consistently used for the interspersed ultrasoundcontrol data frames regardless of scan parameters that are defined forthe user-selected preset; labeling the ultrasound control data frames ascorresponding to the user-selected preset; and sending the labeledultrasound control data frames to a server for adding to a dataset,wherein the dataset can be used to train the artificial intelligencemodel for predicting whether the user-selected preset would be suitablefor later-acquired ultrasound control data frames.
 13. An ultrasoundscanner that controls its settings, the ultrasound scanner comprising aprocessor and computer readable memory storing computer readableinstructions, which, when executed by the processor, cause theultrasound scanner to: acquire an ultrasound image feed by sequentiallyobtaining ultrasound data frames that are converted to viewable imageframes; and during acquisition of the ultrasound image feed: acquire,using reference scan parameters, ultrasound control data frames that areinterspersed amongst the ultrasound data frames, the reference scanparameters being consistently used for the interspersed ultrasoundcontrol data frames regardless of scan parameters that are used foracquiring the ultrasound image feed; and use the ultrasound control dataframes to control the settings of the ultrasound scanner.
 14. Theultrasound scanner of claim 13, wherein the reference scan parametersconsistently used for the interspersed ultrasound control data frameshave one or more of: a fixed depth, a fixed number of acquisition lines,fixed focal zones or a fixed sampling rate.
 15. The ultrasound scannerof claim 13, comprising: a first preset according to which theultrasound image feed is acquired; and a second preset different fromthe first preset; wherein control of the settings of the ultrasoundscanner comprises changing the first preset to the second preset. 16.The ultrasound scanner of claim 15 comprising an artificial intelligencemodel, wherein the ultrasound scanner is configured to: prior tochanging the first preset to the second preset, process at least thelatest of the ultrasound control data frames against the artificialintelligence model to predict a suitable preset for the ultrasound imagefeed; and use the predicted suitable preset as the second preset thatthe ultrasound scanner is changed to.
 17. The ultrasound scanner ofclaim 16, wherein the artificial intelligence model is trained with oneor more datasets containing different classes of ultrasound control dataframes for different presets, and substantially all the ultrasoundcontrol data frames in the one or more datasets are consistentlyacquired using the reference scan parameters.
 18. The ultrasound scannerof claim 17, wherein the different presets comprise presets for at leasttwo of abdomen, cardiac, bladder, lung, obstetrics/gynecology,transcranial, superficial, thyroid, vascular, musculoskeletal, breast,ocular, prostate, fertility, or nerve.
 19. The ultrasound scanner ofclaim 17, wherein the different classes of ultrasound control dataframes comprise ultrasound data acquired of different body parts, thedifferent body parts comprising at least two of: a lung, a heart, aliver, a kidney, a bladder, an eye, a womb, a thyroid gland, a breast, abrain, an artery, a vein, a muscle, an embryo, a tendon, a bone, afetus, a prostate, a uterus, an ovary, testes, a pancreas, or a gallbladder.
 20. The ultrasound scanner of claim 15, further configured to:monitor subsequently acquired ultrasound control data frames todetermine if the subsequently acquired ultrasound control data framescontinue to correspond to the second preset; and after a period of time,label the subsequently acquired ultrasound control data frames asultrasound control data frames that correspond to the second preset, sothat the subsequently acquired ultrasound control data frames can usedfor training or reinforcing an artificial intelligence model.